Overview

Dataset statistics

Number of variables11
Number of observations2930
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory251.9 KiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 4 (0.1%) duplicate rowsDuplicates
Total Bsmt SF has 79 (2.7%) zerosZeros
2nd Flr SF has 1678 (57.3%) zerosZeros
Garage Cars has 157 (5.4%) zerosZeros
Open Porch SF has 1300 (44.4%) zerosZeros

Reproduction

Analysis started2024-01-02 21:48:47.111953
Analysis finished2024-01-02 21:49:01.503226
Duration14.39 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Lot Area
Real number (ℝ)

Distinct1960
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10147.922
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:01.618519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3188.3
Q17440.25
median9436.5
Q311555.25
95-th percentile17131
Maximum215245
Range213945
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation7880.0178
Coefficient of variation (CV)0.77651542
Kurtosis265.02367
Mean10147.922
Median Absolute Deviation (MAD)2040
Skewness12.820898
Sum29733411
Variance62094680
MonotonicityNot monotonic
2024-01-02T22:49:01.809568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 44
 
1.5%
7200 43
 
1.5%
6000 34
 
1.2%
9000 29
 
1.0%
10800 25
 
0.9%
8400 21
 
0.7%
7500 21
 
0.7%
6240 18
 
0.6%
1680 18
 
0.6%
6120 17
 
0.6%
Other values (1950) 2660
90.8%
ValueCountFrequency (%)
1300 1
< 0.1%
1470 1
< 0.1%
1476 1
< 0.1%
1477 2
0.1%
1484 1
< 0.1%
1488 1
< 0.1%
1491 1
< 0.1%
1495 1
< 0.1%
1504 1
< 0.1%
1526 2
0.1%
ValueCountFrequency (%)
215245 1
< 0.1%
164660 1
< 0.1%
159000 1
< 0.1%
115149 1
< 0.1%
70761 1
< 0.1%
63887 1
< 0.1%
57200 1
< 0.1%
56600 1
< 0.1%
53504 1
< 0.1%
53227 1
< 0.1%

Overall Qual
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0948805
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:01.965641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4110261
Coefficient of variation (CV)0.23151005
Kurtosis0.05241245
Mean6.0948805
Median Absolute Deviation (MAD)1
Skewness0.19063396
Sum17858
Variance1.9909946
MonotonicityNot monotonic
2024-01-02T22:49:02.099604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
4 226
 
7.7%
9 107
 
3.7%
3 40
 
1.4%
10 31
 
1.1%
2 13
 
0.4%
1 4
 
0.1%
ValueCountFrequency (%)
1 4
 
0.1%
2 13
 
0.4%
3 40
 
1.4%
4 226
 
7.7%
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
9 107
 
3.7%
10 31
 
1.1%
ValueCountFrequency (%)
10 31
 
1.1%
9 107
 
3.7%
8 350
11.9%
7 602
20.5%
6 732
25.0%
5 825
28.2%
4 226
 
7.7%
3 40
 
1.4%
2 13
 
0.4%
1 4
 
0.1%

Overall Cond
Real number (ℝ)

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5631399
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:02.226338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1115366
Coefficient of variation (CV)0.19980381
Kurtosis1.4914497
Mean5.5631399
Median Absolute Deviation (MAD)0
Skewness0.57442948
Sum16300
Variance1.2355135
MonotonicityNot monotonic
2024-01-02T22:49:02.372774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 1654
56.5%
6 533
 
18.2%
7 390
 
13.3%
8 144
 
4.9%
4 101
 
3.4%
3 50
 
1.7%
9 41
 
1.4%
2 10
 
0.3%
1 7
 
0.2%
ValueCountFrequency (%)
1 7
 
0.2%
2 10
 
0.3%
3 50
 
1.7%
4 101
 
3.4%
5 1654
56.5%
6 533
 
18.2%
7 390
 
13.3%
8 144
 
4.9%
9 41
 
1.4%
ValueCountFrequency (%)
9 41
 
1.4%
8 144
 
4.9%
7 390
 
13.3%
6 533
 
18.2%
5 1654
56.5%
4 101
 
3.4%
3 50
 
1.7%
2 10
 
0.3%
1 7
 
0.2%

Year Built
Real number (ℝ)

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.3563
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:02.547932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11954
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.245361
Coefficient of variation (CV)0.015342412
Kurtosis-0.50171504
Mean1971.3563
Median Absolute Deviation (MAD)25
Skewness-0.60446222
Sum5776074
Variance914.78184
MonotonicityNot monotonic
2024-01-02T22:49:02.731627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.8%
2006 138
 
4.7%
2007 109
 
3.7%
2004 99
 
3.4%
2003 88
 
3.0%
1977 57
 
1.9%
1920 57
 
1.9%
1976 54
 
1.8%
1999 52
 
1.8%
2008 49
 
1.7%
Other values (108) 2085
71.2%
ValueCountFrequency (%)
1872 1
 
< 0.1%
1875 1
 
< 0.1%
1879 1
 
< 0.1%
1880 5
0.2%
1882 1
 
< 0.1%
1885 2
 
0.1%
1890 7
0.2%
1892 2
 
0.1%
1893 1
 
< 0.1%
1895 3
0.1%
ValueCountFrequency (%)
2010 3
 
0.1%
2009 25
 
0.9%
2008 49
 
1.7%
2007 109
3.7%
2006 138
4.7%
2005 142
4.8%
2004 99
3.4%
2003 88
3.0%
2002 47
 
1.6%
2001 35
 
1.2%

Total Bsmt SF
Real number (ℝ)

ZEROS 

Distinct1058
Distinct (%)36.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1051.6145
Minimum0
Maximum6110
Zeros79
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:02.917775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile453
Q1793
median990
Q31302
95-th percentile1776
Maximum6110
Range6110
Interquartile range (IQR)509

Descriptive statistics

Standard deviation440.61507
Coefficient of variation (CV)0.41898913
Kurtosis9.1356123
Mean1051.6145
Median Absolute Deviation (MAD)236
Skewness1.1562043
Sum3080179
Variance194141.64
MonotonicityNot monotonic
2024-01-02T22:49:03.102412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
2.7%
864 74
 
2.5%
672 29
 
1.0%
912 26
 
0.9%
1040 25
 
0.9%
768 24
 
0.8%
816 23
 
0.8%
728 21
 
0.7%
780 19
 
0.6%
384 19
 
0.6%
Other values (1048) 2590
88.4%
ValueCountFrequency (%)
0 79
2.7%
105 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
190 1
 
< 0.1%
192 1
 
< 0.1%
216 2
 
0.1%
240 1
 
< 0.1%
245 1
 
< 0.1%
264 4
 
0.1%
ValueCountFrequency (%)
6110 1
< 0.1%
5095 1
< 0.1%
3206 1
< 0.1%
3200 1
< 0.1%
3138 1
< 0.1%
3094 1
< 0.1%
2846 1
< 0.1%
2660 1
< 0.1%
2633 1
< 0.1%
2630 1
< 0.1%

1st Flr SF
Real number (ℝ)

Distinct1083
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.5577
Minimum334
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:03.285348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile665.45
Q1876.25
median1084
Q31384
95-th percentile1829.55
Maximum5095
Range4761
Interquartile range (IQR)507.75

Descriptive statistics

Standard deviation391.89089
Coefficient of variation (CV)0.33796584
Kurtosis6.9688085
Mean1159.5577
Median Absolute Deviation (MAD)236
Skewness1.4694286
Sum3397504
Variance153578.47
MonotonicityNot monotonic
2024-01-02T22:49:03.470801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 46
 
1.6%
1040 28
 
1.0%
912 19
 
0.6%
816 18
 
0.6%
848 18
 
0.6%
960 18
 
0.6%
672 17
 
0.6%
894 17
 
0.6%
936 17
 
0.6%
546 15
 
0.5%
Other values (1073) 2717
92.7%
ValueCountFrequency (%)
334 1
 
< 0.1%
372 1
 
< 0.1%
407 1
 
< 0.1%
432 1
 
< 0.1%
438 1
 
< 0.1%
442 1
 
< 0.1%
448 1
 
< 0.1%
453 1
 
< 0.1%
480 1
 
< 0.1%
483 13
0.4%
ValueCountFrequency (%)
5095 1
< 0.1%
4692 1
< 0.1%
3820 1
< 0.1%
3228 1
< 0.1%
3138 1
< 0.1%
2898 1
< 0.1%
2726 1
< 0.1%
2696 1
< 0.1%
2674 1
< 0.1%
2633 1
< 0.1%

2nd Flr SF
Real number (ℝ)

ZEROS 

Distinct635
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.45597
Minimum0
Maximum2065
Zeros1678
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:03.651965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3703.75
95-th percentile1130.1
Maximum2065
Range2065
Interquartile range (IQR)703.75

Descriptive statistics

Standard deviation428.39572
Coefficient of variation (CV)1.277055
Kurtosis-0.4148613
Mean335.45597
Median Absolute Deviation (MAD)0
Skewness0.86645675
Sum982886
Variance183522.89
MonotonicityNot monotonic
2024-01-02T22:49:03.839787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1678
57.3%
546 23
 
0.8%
728 18
 
0.6%
504 17
 
0.6%
720 13
 
0.4%
600 13
 
0.4%
672 13
 
0.4%
896 11
 
0.4%
886 10
 
0.3%
756 9
 
0.3%
Other values (625) 1125
38.4%
ValueCountFrequency (%)
0 1678
57.3%
110 1
 
< 0.1%
125 1
 
< 0.1%
144 1
 
< 0.1%
167 1
 
< 0.1%
180 1
 
< 0.1%
182 1
 
< 0.1%
185 1
 
< 0.1%
192 1
 
< 0.1%
208 2
 
0.1%
ValueCountFrequency (%)
2065 1
< 0.1%
1872 1
< 0.1%
1862 1
< 0.1%
1836 1
< 0.1%
1818 1
< 0.1%
1796 1
< 0.1%
1788 1
< 0.1%
1778 1
< 0.1%
1721 1
< 0.1%
1629 1
< 0.1%

Fireplaces
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
0
1422 
1
1274 
2
221 
3
 
12
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row0
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Length

2024-01-02T22:49:03.998555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-02T22:49:04.136153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2930
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Garage Cars
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7668146
Minimum0
Maximum5
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:04.270802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76056636
Coefficient of variation (CV)0.43047321
Kurtosis0.24496945
Mean1.7668146
Median Absolute Deviation (MAD)0
Skewness-0.21983636
Sum5175
Variance0.5784612
MonotonicityNot monotonic
2024-01-02T22:49:04.413465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1603
54.7%
1 778
26.6%
3 374
 
12.8%
0 157
 
5.4%
4 16
 
0.5%
5 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 157
 
5.4%
1 778
26.6%
2 1603
54.7%
3 374
 
12.8%
4 16
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 16
 
0.5%
3 374
 
12.8%
2 1603
54.7%
1 778
26.6%
0 157
 
5.4%

Open Porch SF
Real number (ℝ)

ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.533447
Minimum0
Maximum742
Zeros1300
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:04.597191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27
Q370
95-th percentile182.55
Maximum742
Range742
Interquartile range (IQR)70

Descriptive statistics

Standard deviation67.4834
Coefficient of variation (CV)1.4197035
Kurtosis10.954343
Mean47.533447
Median Absolute Deviation (MAD)27
Skewness2.5353859
Sum139273
Variance4554.0093
MonotonicityNot monotonic
2024-01-02T22:49:04.818542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1300
44.4%
36 52
 
1.8%
48 52
 
1.8%
40 44
 
1.5%
32 38
 
1.3%
24 36
 
1.2%
28 35
 
1.2%
20 33
 
1.1%
30 31
 
1.1%
60 30
 
1.0%
Other values (242) 1279
43.7%
ValueCountFrequency (%)
0 1300
44.4%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
0.1%
11 3
 
0.1%
12 5
 
0.2%
15 2
 
0.1%
16 15
 
0.5%
17 2
 
0.1%
ValueCountFrequency (%)
742 1
< 0.1%
570 1
< 0.1%
547 1
< 0.1%
523 1
< 0.1%
502 1
< 0.1%
484 1
< 0.1%
444 1
< 0.1%
418 1
< 0.1%
406 1
< 0.1%
382 1
< 0.1%

SalePrice
Real number (ℝ)

Distinct1032
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180796.06
Minimum12789
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2024-01-02T22:49:05.016665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12789
5-th percentile87500
Q1129500
median160000
Q3213500
95-th percentile335000
Maximum755000
Range742211
Interquartile range (IQR)84000

Descriptive statistics

Standard deviation79886.692
Coefficient of variation (CV)0.4418608
Kurtosis5.1189
Mean180796.06
Median Absolute Deviation (MAD)37000
Skewness1.7435001
Sum5.2973246 × 108
Variance6.3818836 × 109
MonotonicityNot monotonic
2024-01-02T22:49:05.220646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135000 34
 
1.2%
140000 33
 
1.1%
130000 29
 
1.0%
155000 28
 
1.0%
145000 26
 
0.9%
160000 23
 
0.8%
185000 21
 
0.7%
110000 21
 
0.7%
115000 20
 
0.7%
120000 20
 
0.7%
Other values (1022) 2675
91.3%
ValueCountFrequency (%)
12789 1
< 0.1%
13100 1
< 0.1%
34900 1
< 0.1%
35000 1
< 0.1%
35311 1
< 0.1%
37900 1
< 0.1%
39300 1
< 0.1%
40000 1
< 0.1%
44000 1
< 0.1%
45000 1
< 0.1%
ValueCountFrequency (%)
755000 1
< 0.1%
745000 1
< 0.1%
625000 1
< 0.1%
615000 1
< 0.1%
611657 1
< 0.1%
610000 1
< 0.1%
591587 1
< 0.1%
584500 1
< 0.1%
582933 1
< 0.1%
556581 1
< 0.1%

Interactions

2024-01-02T22:48:59.733593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:47.634867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.911920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.191953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.700911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.967787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.276204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.547779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.756328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:58.427369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.853257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:47.774718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.024697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.319752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.821476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.087684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.395741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.663463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.871678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:58.554456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.972520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:47.902717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.135645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.440764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.940131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.208680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.516045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.779281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.985386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:58.682935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.112626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.048190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.267894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.577164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.078256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.353950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.655801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.925605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.116522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:58.830336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.246093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.186528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.389726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.722159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.203887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.486403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.787340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.049857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.245189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:58.965926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.377193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.311838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.512092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.865744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.333999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.615142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.915311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.170937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.372649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.097881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.499451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.425319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.626612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.988798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.452916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.736524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.034423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.280393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.494076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.219576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.618492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.536809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.745956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.112361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.572916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.868475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.151929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.391805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.614603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.339823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.751210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.654329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:49.871822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.420029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.696505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:53.990432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.277081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.505336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.791416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.466960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:49:00.894722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:48.781755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:50.064539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:51.562329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:52.831773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:54.133826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:55.411869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:56.631000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:57.932315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-02T22:48:59.597466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-02T22:49:01.085385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-02T22:49:01.324841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Lot AreaOverall QualOverall CondYear BuiltTotal Bsmt SF1st Flr SF2nd Flr SFFireplacesGarage CarsOpen Porch SFSalePrice
0317706519601080.01656022.062215000
111622561961882.0896001.00105000
2142676619581329.01329001.036172000
3111607519682110.02110022.00244000
413830551997928.092870112.034189900
59978661998926.092667812.036195500
649208520011338.01338002.00213500
750058519921280.01280002.082191500
853898519951595.01616012.0152236500
97500751999994.0102877612.060189000
Lot AreaOverall QualOverall CondYear BuiltTotal Bsmt SF1st Flr SF2nd Flr SFFireplacesGarage CarsOpen Porch SFSalePrice
29201894451970546.054654601.02471000
2921126406519761728.01728002.00150900
292292975519761728.01728002.00188000
2923174005519771126.01126012.041160000
2924200005719601224.01224012.00131000
292579376619841003.01003002.00142500
29268885551983864.0902002.00131000
292710441551992912.0970000.032132000
2928100105519741389.01389012.038170000
29299627751993996.0996100413.048188000

Duplicate rows

Most frequently occurring

Lot AreaOverall QualOverall CondYear BuiltTotal Bsmt SF1st Flr SF2nd Flr SFFireplacesGarage CarsOpen Porch SFSalePrice# duplicates
03675652005547.01072002.0441400002
145908520061554.01554012.0732095002
270185519790.01535002.001188582
3108005519871200.01200000.001790002